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    20130812 icatapult-130812090501-phpapp02 20130812 icatapult-130812090501-phpapp02 Presentation Transcript

    • Startup Metrics, a love story. Everything you need to know about Startup Product Metrics. #iCatapult Workshop - 2013-08-12 Slideshare Exclusive: The full Powerdeck. ;) @andreasklinger
    • @andreasklinger “Startup Founder” “Product Guy” @andreasklinger
    • @andreasklinger “Startup Founder” “Product Guy” What we will cover - Early Stage Metrics (Pre Product Market Fit). - Create your dashboard & customer journey map. - Wrong assumptions on Metrics - Lean Analytics & Advanced Topics @andreasklinger
    • The Biggest Risk of a Startup? #emminvest – @andreasklinger
    • The Biggest Risk of a Startup? Time/Money? No You will learn a lot + huge network gain. #emminvest – @andreasklinger
    • The Biggest Risk of a Startup? Building ALMOST the right thing. Seeing the successful new competitor who does what you do, but has one little detail different that you never focused on. #emminvest – @andreasklinger
    • “Startups don’t fail because they lack a product; they fail because they lack customers and a profitable business model” Steve Blank And it might have been where you didn’t look. @andreasklinger
    • To miss your opportunity. By focusing on the wrong thing. Market Customer Job/Problem Willing to pay Your Solution @andreasklinger
    • Or by looking at the wrong customer. Market Other Market Customer Job/Problem Willing to pay competitor competitor competitor mer usto blem C Pro Job/ g to pay Willin competitor Your Solution @andreasklinger
    • Startup Founders. @andreasklinger
    • Some startups have ideas for a new product. Looking for customers to buy (or at least use) it. Customers don’t buy. “early stage” @andreasklinger
    • traction Product/Market Fit time With early stage I do not mean “X Years” I mean before product/market fit. @andreasklinger
    • Product/market fit Being in a good market with a product that can satisfy that market. ~ Marc Andreessen @andreasklinger
    • Product/market fit Being in a good market with a product that can satisfy that market. ~ Marc Andreessen = People want your stuff. @andreasklinger
    • traction Product/Market Fit time @andreasklinger
    • traction Product/Market Fit time Discovery Validation Efficiency Scale Steve Blank - Customer Development @andreasklinger
    • traction Product/Market Fit time Empathy Stickness Virality Revenue Scale Ben Yoskovitz, Alistair Croll - Lean Analytics @andreasklinger
    • traction Product/Market Fit time Empathy Stickness Virality Revenue Scale Find a product the market wants. @andreasklinger
    • traction Product/Market Fit time Empathy Stickness Find a product the market wants. Virality Revenue Scale Optimise the product for the market. @andreasklinger
    • traction Product/Market Fit time Empathy Stickness Virality Find a product the market wants. People in search for new product start here. Revenue Scale Optimise the product for the market. Most clones start here. @andreasklinger
    • traction Product/Market Fit time Empathy Stickness Product & Customer Development Virality Revenue Scale Scale Marketing & Operations @andreasklinger
    • traction Product/Market Fit time Empathy Stickness Virality Revenue Scale Startups have phases but they overlap. @andreasklinger
    • traction Product/Market Fit time Empathy Stickness Virality Revenue Scale 83% of all startups are in here. @andreasklinger
    • traction Product/Market Fit time Empathy Stickness Virality 83% of all startups are in here. Revenue Scale Most stuff we learn about web analytics is meant for this part @andreasklinger
    • Most of the techniques/pattern we find when we look to learn about metrics come out from Online Marketing @andreasklinger
    • A/B Testing, Funnels, Referral Optimization, etc etc They are about optimizing, not innovating @andreasklinger
    • What are we testing here? @andreasklinger
    • What are we testing here? Value Proposition, Communication. Maybe Channels, Target Group. Not the product implementation. @andreasklinger
    • We need product insights. @andreasklinger
    • Good news: We don’t need many people It’s too early for optimizations. Challenge #1: Get them to stay. @andreasklinger
    • “...interesting...”
    • What does this mean for my product? Are we even on the right track?
    • eg. What is a good Time on Site? Maybe users spend time reading your support pages just because they are super confused.
    • Google Analytics is meant for Referral Optimization. Where does traffic come from? Is this traffic of value?
    • What does it mean anyway? @andreasklinger
    • What does it mean anyway? “We have 50k registered users!” Do they still use the service? Are they the right people? “We have 5000 newsletter signups!” Do they react? Are they potential customers? “We have 500k app downloads!” Do they still use the app? @andreasklinger
    • Some graphs always go up. Vanity. Use it for the Press. Not for your product. @andreasklinger
    • It’s easy to improve conversions (of the wrong people) Source: http://www.codinghorror.com/blog/2009/07/how-not-to-advertise-on-the-internet.html @andreasklinger
    • It’s easy to improve conversions (of the wrong people) This is a real example. Thank the Internet. Source: http://www.codinghorror.com/blog/2009/07/how-not-to-advertise-on-the-internet.html @andreasklinger
    • The same is true for funnels. It’s easy to optimize by pushing the wrong people forward. Disclaimer: this is not a real example. btw newrelic is awesome ;) @andreasklinger
    • The same is true for funnels. It’s easy to optimize by pushing the wrong people forward. Disclaimer: this is not a real example. btw newrelic is awesome ;) @andreasklinger
    • “Small Data” something small Early Stage Product Metrics suck: - We have the wrong product suck. why early stage metrics - With the wrong communication - Attacting the wrong targetgroup - Who provide us too few datapoints Most likely @andreasklinger
    • Early stage product metrics get easily affected by external traffic. One of our main goals is to minimize that effect. @andreasklinger
    • #1 Values: 0, 0, 0, 100 What’s the average? @andreasklinger
    • #1 Values: 0, 0, 0, 100 What’s the average? 25 What’s the median? @andreasklinger
    • #1 Values: 0, 0, 0, 100 What’s the average? 25 What’s the median? 0 Eg. “Average Interactions” can be Bollocks #scb13 – @andreasklinger
    • #1 Values: 0, 0, 0, 100 What’s the average? 25 What’s the median? 0 Eg. “Average Interactions” can be Bollocks #scb13 – @andreasklinger
    • #2 Values: 0, 5, 10 What’s the average? @andreasklinger
    • #2 Values: 0, 5, 10 What’s the average? 5 What’s the standard deviation? 4,08 => “Average” hides information. #scb13 – @andreasklinger
    • #3 “This is not statistical significant.” I don’t care. We are anyway playing dart in a dark room. Let’s try to get it as good as possible and use our intuation for the rest. @andreasklinger
    • Correlation / Causality #scb13 – @andreasklinger Source: Lean Analytics
    • Correlation / Causality Source:@andreasklinger Lean Analytics
    • How can we use metrics? @andreasklinger
    • How can we use metrics? To Explore (Examples) To Report (Examples) Investigate an assumption. Measure Progress. (Accounting) Look for causalities. Measure Feature Impact. Validate customer feedback. See customer happiness/ health. Validate internal opinions. @andreasklinger
    • Hits Page Views Visits Visitors People Views Clicks Conversions Engagement What do i measure? TL;DR: In case of doubt, people. @andreasklinger
    • What are good KPIs? Linked to assumptions of your product (validation/falsify) Framework: AARRR #scb13 – @andreasklinger
    • What are good KPIs? Rate or Ratio (0.X or %) Framework: AARRR #scb13 – @andreasklinger
    • What are good KPIs? Framework: AARRR Comparable (To your history (or a/b). Forget the market) #scb13 – @andreasklinger
    • What are good KPIs? Framework: AARRR Explainable (If you don’t get it it means nothing) #scb13 – @andreasklinger
    • What are good KPIs? Linked to assumptions of your product (validation/falsify) Rate or Ratio (0.X or %) Framework: AARRR Comparable (To your history (or a/b). Forget the market) Explainable (If you don’t get it it means nothing) #scb13 – @andreasklinger
    • “Industry Standards” Framework: AARRR Use industry averages as reality check. Not as benchmark. - Usually very hard to get. - Everyone defines stuff different. - You might end up with another business model anyway. - Compare yourself vs your history data. #scb13 – @andreasklinger
    • Let’s start. #scb13 – @andreasklinger
    • Segment Users into Cohorts Cohorts = Groups of people that share attributes. @andreasklinger
    • Segment Users into Cohorts Like it People 23% @andreasklinger
    • Segment Users into Cohorts Like it People 0-25 3% People 26-50 4% People 51-75 65% @andreasklinger
    • Segment Users into Cohorts Average Spending Jan €5 Feb €4.5 Mar €5 Apr €4.25 May €4.5 ... ... Averages can hide patterns. @andreasklinger
    • Segment Users into Cohorts Month Lifecycle Registration Month 1 2 3 4 5 Jan €5 €3 €2 €1 €0.5 Feb €6 €4 €2 €1 Mar €7 €6 €5 Apr €8 €7 May €9 ... ... Insight: Users spend less over time in average. We still don’t know if they spend less, or if less people spend at all. @andreasklinger
    • Apply a framework: AARRR @andreasklinger
    • Acquisition Visit / Signup / etc Activation Use of core feature Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure
    • Example: Blossom.io Kanban Project Mangement Tool (people who) registered an account Acquisition created a card Activation moved a card Retention invited team members Referral have upgraded Revenue
    • Example: Photoapp (people who) registered an account Acquisition created first photo Activation opened app twice in perioid Retention shared photo to fb Referral ??? (exit?) Revenue
    • Example: Photoapp ARRR acquisition activation retention referral revenue registration first photo twice a month share … 8750 65% 23% 9%
    • Example: Photoapp Cohorts based on registration week AARRR WK acquisition activation Photoapp registration first photo retention referral revenue twice a month share … 1 400 62,5% 25% 10% 2 575 65% 23% 9% 3 350 64% 26% 4% … … … … …
    • Acquisition Visit / Signup / etc Activation Use of core feature Which Metrics to focus on? Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure
    • Acquisition Visit / Signup / etc Activation Use of core feature Short Answer: Focus on Retention Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure
    • Because Retention = f(user_happiness)
    • Because Retention = f(user_happiness) Not only “visited again”. But “did core activity X again”
    • Acquisition Visit / Signup / etc Activation Use of core feature Long answer - It depends on two things: Retention Come + use again Phase of company Referral Invite + Signup Type of Product Revenue Engine of Growth $$$ Earned (c) Dave McClure
    • #1 Phase Acquisition Visit / Signup / etc Activation Use of core feature Retention Come + use again Referral Invite + Signup Revenue $$$ Earned Source: Lean Analytics Book - highly recommend
    • #2 Engine of Growth Acquisition Visit / Signup / etc Paid Activation Make more money on a customer than you spend, to buy new ones. Eg. Saas Use of core feature Viral Retention A Users brings more than one new user. Eg. typical interactive ad-campaigns Come + use again Referral Sticky Invite + Signup Keep your userbase to improve your quality. Eg. Communities Revenue $$$ Earned @andreasklinger
    • #2 Type of Company (linked to Engine of Growth) Acquisition Visit / Signup / etc Saas Build a better product Activation Use of core feature Social Network/Community “(subjective) Critical Mass” Marketplace Get the right sellers and many more... Retention Come + use again Referral Invite + Signup Revenue $$$ Earned @andreasklinger
    • Acquisition Visit / Signup / etc Activation Use of core feature Short Answer: Focus on Retention Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure
    • Retention Matrix Stickness over lifetime. Source: www.usercycle.com
    • Acquisition Visit / Signup / etc Activation Use of core feature Short Answer: Focus on Retention Eg. Did core-action X times in period. Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure
    • Dig deeper: Crashpadder’s Happiness Index e.g. Weighted sum over core activities by hosts. Cohorts by cities and time. = Health/Happiness Dashboard
    • Marketplaces / 2-sided models
    • Marketplaces / 2-sided models
    • Marketplaces / 2-sided models Amazon Approach: 1) Create loads of inventory for longtail search 2) Create demand 3) Have shitloads of money. 4) Wait and win. Boticca (aka Startup) Approach: 1) Focus on one niche Targetgroup 2) Create “enough” inventory with very few sellers 3) Create demand 4) Create more demand 5) Add a few more sellers. Repeat. Pray.
    • How to solve the Chicken and Egg Problem TL;DR: You need very few but very good and happy chickens to get a lot of eggs. Btw: If the chickens don’t come by themselves, buy them. Uber paid 30$/h to the first drivers. Ignore the “minor chickens”, they come anyway.
    • Example Mobile App: Pusher2000 Trainer2peer pressure sport app (prelaunch “beta”). Rev channel: Trainers pay monthly fee. Two sided => Segment AARRR for both sides (trainer/user) Marketplace => Value = Transactions / Supplier Social Software => DAU/MAU to see if activated users stay active Chicken/Egg => You need a few very happy chickens for loads of eggs. Activated User:AARRR two training sessions Framework: More than Week/Week retention to see if public launch makes sense Optimize retention: Interviews with Users that left Measure Trainer Happiness Index Pushups / User / Week to see if the core assumption (People will do more pushups) is valid #scb13 – @andreasklinger
    • Groupwork - What stage are you in? Empathy Stickness Framework: AARRR Virality Revenue Scale #scb13 – @andreasklinger
    • Groupwork - Define your AARRR Metrics! Acquisition, Activation, Retention, Referral, Revenue Framework: (e.g. User who xxxx) One KPI each. AARRR #scb13 – @andreasklinger
    • Groupwork - What’s your current core metric. Framework: AARRR #scb13 – @andreasklinger
    • Show & Tell. @andreasklinger
    • The dirty side of metrics. @andreasklinger
    • Metrics need to hurt @andreasklinger
    • Metrics need to hurt A Startup should always only focus on one core metric to optimize. Your dashboard should only show metrics you are ashamed of. @andreasklinger
    • Metrics need to hurt Have two dashboards (example bufferApp): A actionable board to work on your product (eg. % people buffering tweets in week 2) A vanity board one to show to investors and tell to the press (eg. total amount of tweets buffered) @andreasklinger
    • Metrics need to hurt If you are not ashamed about the KPIs in your product dashboard than something is wrong. Either you focus on the wrong KPIs. OR you do not drill deep enough. @andreasklinger
    • Metrics need to hurt Example: Garmz/LOOKK Great Numbers: 90% activation (activation = vote) But they only voted for friends instead of actually using the platform. We drilled (not far) deeper: Activation = Vote for 2 different designers. Boom. Pain. Metrics need to hurt. @andreasklinger
    • Let’s talk cash. @andreasklinger
    • Let’s talk cash: Life Time Value (LTV) Life Time = 1/Churn Eg. 1 / 0.08 = 12.5 months Average Revenue Per User (ARPU) = Revenue / Amount of Users Eg. 20.000€ / 1500 = 13.33€ LTV = ARPU * LifeTime = 13.33€ * 12.5 = 166.6666 @andreasklinger
    • Let’s talk cash: Customer Acquisition Costs CAC = Total Acquisition Costs this month / New Customers this month Goal: CAC < LTV even better: 2-3xCAC < LTV @andreasklinger
    • Monitor Revenue MRR Monthly Recurring Revenue Revenue Churn & Qty Churn Source: www.usercycle.com @andreasklinger
    • But how to make more money? @andreasklinger
    • Big wins are often cheap. source: thomas fuchs - www.metricsftw.com
    • Viral Distribution @andreasklinger
    • Viral Distribution 3 kinds of virality Product Inherent A function of use. Eg. Dropbox sharing Product Artificial Added to support behaviour. E.g. Reward systems Word of Mouth Function of customer satisfaction. Source: Lean Analytics
    • Viral Distribution Invitation Rate = Invited people / inviters eg. 1250 users invited 2000 people = 1.6 Acceptance Rate = Invited Signed-up / Invited total eg. out of the 2000 people 580 signed up = 0.29 Viral Coefficient = Invitation Rate * Acceptance Rate eg. 1.6 * 0.29 = 0.464 (every customer will bring half a customer additionally)
    • Viral Distribution Important #1: Unless your Growth Engine is Viral, don’t focus early on Viral Factors. Focus on retention/stickyness. Important #2: Viral Cycle Time (time between recv invite and sending invite) is extremely important. eg. K = 2 Cycle Time: 2 days => 20 days = 20.470 users Cycle Time: 1 day => 20 days = 20mio users Source: http://www.forentrepreneurs.com/lessons-learnt-viral-marketing/
    • Churn is not what you think it is #scb13 – @andreasklinger @andreasklinger
    • Churn is not what you think it is “5% Churnrate per month” One of the highest churn reasons is expired credit cards. Sad Fact: People forget about you. #scb13 – @andreasklinger @andreasklinger
    • Churn is not what you think it is People don’t wake up and suddenly want to unsubscribe your service... “Oh.. it’s May 5th.. Better churn from that random online startup i found 2 months ago.” #scb13 – @andreasklinger @andreasklinger
    • Churn is not what you think it is You loose them in the first 3 weeks. And then at some point just remind them to unsubscribe. That’s when we measure it (too late). #scb13 – @andreasklinger Source: Intercom.io
    • Churn is not what you think it is #scb13 – Lean Analytics Source: @andreasklinger
    • Checkout Intercom.io Segment + message customers = Awesome #scb13 – @andreasklinger
    • User activation. Some users are happy (power users) Some come never again (churned users) What differs them? It’s their activities in their first 30 days. @andreasklinger
    • Sub-funnels “What did the people do that signed up, before they signed up, compare to those who didn’t?” Source: Usercycle
    • Example Twitter How often did activated users use twitter in the first month: 7 times What did they do? Follow 20 people, followed back by 10 Churn: If they don’t keep them 7 times in the first 30 days. They will lose them forever. It doesn’t matter when a user remembers to unsubscribe #scb13 – @andreasklinger
    • Example Twitter Example Twitter: How did they get more people to follow 30people within 7visits in the first 30 days? Ran assumptions, created features and ran experiments! Watch: http://www.youtube.com/watch?v=L2snRPbhsF0 #scb13 – @andreasklinger
    • Data is noisy. @andreasklinger
    • Data is noisy. source: thomas fuchs - www.metricsftw.com
    • Data is noisy. source: thomas fuchs - www.metricsftw.com
    • Data is noisy. source: thomas fuchs - www.metricsftw.com
    • Data is noisy. source: thomas fuchs - www.metricsftw.com
    • Data is noisy. source: thomas fuchs - www.metricsftw.com
    • Dataschmutz A layer of dirt obfuscating your useable data. Usually “wrong intent”. Usually our fault. (~ sample noise we created ourselves) @andreasklinger
    • Dataschmutz A layer of dirt obfuscating your useable data. @andreasklinger
    • Dataschmutz A layer of dirt obfuscating your useable data. e.g. Traffic Spikes of wrong customer segment. (wrong intent) More “wrong” people come. Lower conversion rate on the landing page. @andreasklinger
    • How to minimize the impact of Dataschmutz Base your KPIs on wavebreakers. WK visitors acquisition activation retention referral revenue twice a month share … Birchbox visit registration first photo 1 6000 66% / 4000 62,5% 25% 10% 2 25000 35% / 8750 65% 23% 9% 3 5000 70% / 3500 64% 26% 4%
    • Dataschmutz MySugr is praised as “beautiful app” example.… => Downloads => Problem: Not all are diabetic They focus on people who activated. @andreasklinger
    • Dataschmutz Competitions create artificial incentive Competition Created “Dataschmutz” Competitions (before P/M Fit) are nothing but Teflon Marketing “Would you use my app and might win 1.000.000 USD?” * Users had huge extra incentive. * Marketing People leave. People come.can hurt your numbers. * While we decided on how to They leave dirt in your database. relaunch we had dirty numbers. @andreasklinger
    • Lean Analytics @andreasklinger
    • Lean Analytics @andreasklinger
    • Lean Analytics
    • Lean Analytics Hypothesis: We believe that introducing a newsfeed will increase interaction between users. Track: Engagement between users (comments/likes) Falsifiable Hypothesis: People who visited the newsfeed will give a 30% more comments and likes, than people who didn’t.
    • Lean Analytics Important #1: Don’t forget to timebox experiments. Important #2: Worry less about statistical significance (while early stage). Just use experiments to doublecheck your entrepreneurial intuition.
    • AARRR misses something @andreasklinger
    • CUSTOMER INTENT Acquisition Activation Retention Referral Revenue FULFILMENT OF CUSTOMER INTENT (c) Dave McClure
    • CUSTOMER INTENT (JOB) Acquisition Customer Interviews Activation Retention Customer Interviews & Metrics Referral Revenue FULFILMENT OF CUSTOMER INTENT (c) Dave McClure
    • That’s you using metrics “Look ma, the light started blinking” @andreasklinger
    • Metrics are horrible way to understand customer intent Customer Intent = His “Job to be done” Products are bought because they solve a “job to be done”. Learn about Jobs to be done Framework Watch: http://bit.ly/cc-jtbd @andreasklinger
    • Metrics are horrible way to understand customer intent Great Way: Customer Interviews But: We bias our people, when we ask them. Even if we try not to. Reason: we believe our own bullshit. Watch: www.hackertalks.io
    • Source: Lukas Fittl - http://speakerdeck.com/lfittl
    • Groupwork. - Draw your customer journey - Pick a point where there is likely a problem - Formulate assumption Framework: AARRR - Formulate Success Criteria #scb13 – @andreasklinger
    • Show & Tell. @andreasklinger
    • Summary @andreasklinger
    • Summary - Use Metrics for Product and Customer Development. - Use Cohorts. - Use AARRR. - Figure Customer Intent through non-biasing interviews. - Understand your type of product and it’s core drivers - Find KPIs that mean something to your specific product. - Avoid Telfonmarketing (eg Campaigns pre-product). - Filter Dataschmutz - Metrics need to hurt - Focus on the first 30 days of customer activation. - Connect Product Hypotheses to Metrics. - Don’t hide behind numbers. TL;DR: Use metrics to validate/doublecheck. Use those insights when designing for/speaking to your customers.
    • Read on Startup metrics for Pirates by Dave McClure http://www.slideshare.net/dmc500hats/startup-metrics-for-pirates-long-version Actionable Metrics by Ash Mauyra http://www.ashmaurya.com/2010/07/3-rules-to-actionable-metrics/ Data Science Secrets by DJ Patil - LeWeb London 2012 http://www.youtube.com/watch?v=L2snRPbhsF0 Twitter sign up process http://www.lukew.com/ff/entry.asp?1128 Lean startup metrics - @stueccles http://www.slideshare.net/stueccles/lean-startup-metrics Cohorts in Google Analytics - @serenestudios http://danhilltech.tumblr.com/post/12509218078/startups-hacking-a-cohort-analysis-with-google Rob Fitzpatrick’s Collection of best Custdev Videos - @robfitz http://www.hackertalks.io Lean Analytics Book http://leananalyticsbook.com/introducing-lean-analytics/ Actionable Metrics - @lfittl http://www.slideshare.net/lfittl/actionable-metrics-lean-startup-meetup-berlin App Engagement Matrix - Flurry http://blog.flurry.com/bid/90743/App-Engagement-The-Matrix-Reloaded My Blog http://www.klinger.io
    • Thank you Liked it? Tweet it. @andreasklinger Slides: http://slideshare.net/andreasklinger All pictures: http://flickr.com/commons @andreasklinger